The Pushshift Reddit Dataset
January 23, 2020 ยท Declared Dead ยท ๐ International Conference on Web and Social Media
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, Jeremy Blackburn
arXiv ID
2001.08435
Category
cs.SI: Social & Info Networks
Cross-listed
cs.CY
Citations
992
Venue
International Conference on Web and Social Media
Last Checked
1 month ago
Abstract
Social media data has become crucial to the advancement of scientific understanding. However, even though it has become ubiquitous, just collecting large-scale social media data involves a high degree of engineering skill set and computational resources. In fact, research is often times gated by data engineering problems that must be overcome before analysis can proceed. This has resulted recognition of datasets as meaningful research contributions in and of themselves. Reddit, the so called "front page of the Internet," in particular has been the subject of numerous scientific studies. Although Reddit is relatively open to data acquisition compared to social media platforms like Facebook and Twitter, the technical barriers to acquisition still remain. Thus, Reddit's millions of subreddits, hundreds of millions of users, and hundreds of billions of comments are at the same time relatively accessible, but time consuming to collect and analyze systematically. In this paper, we present the Pushshift Reddit dataset. Pushshift is a social media data collection, analysis, and archiving platform that since 2015 has collected Reddit data and made it available to researchers. Pushshift's Reddit dataset is updated in real-time, and includes historical data back to Reddit's inception. In addition to monthly dumps, Pushshift provides computational tools to aid in searching, aggregating, and performing exploratory analysis on the entirety of the dataset. The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Social & Info Networks
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
node2vec: Scalable Feature Learning for Networks
R.I.P.
๐ป
Ghosted
Cooperative Game Theory Approaches for Network Partitioning
R.I.P.
๐ป
Ghosted
From Louvain to Leiden: guaranteeing well-connected communities
R.I.P.
๐ป
Ghosted
Fake News Detection on Social Media: A Data Mining Perspective
R.I.P.
๐ป
Ghosted
Heterogeneous Graph Attention Network
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted